First draft of model card

README.md

@@ -0,0 +1,103 @@
+---
+language: en
+tags:
+- tapas
+- sequence-classification
+license: apache-2.0
+datasets:
+- tab_fact
+
+---
+
+# TAPAS medium model fine-tuned on Tabular Fact Checking (TabFact) 
+
+This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_medium_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
+This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned on [TabFact](https://github.com/wenhuchen/Table-Fact-Checking). It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table).
+
+The other (non-default) version which can be used is the one with absolute position embeddings:
+- `no_reset`, which corresponds to `tapas_tabfact_inter_masklm_medium`
+
+Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
+the Hugging Face team and contributors.
+
+## Model description
+
+TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. 
+This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
+can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
+was pretrained with two objectives:
+
+- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in 
+  the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words. 
+  This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, 
+  or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional 
+  representation of a table and associated text.
+- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating 
+  a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence 
+  is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.
+
+This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used 
+to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed
+or refuted by the contents of a table. Fine-tuning is done by adding a classification head on top of the pre-trained model, and then 
+jointly train this randomly initialized classification head with the base model on TabFact. 
+
+
+## Intended uses & limitations
+
+You can use this model for classifying whether a sentence is supported or refuted by the contents of a table.
+
+For code examples, we refer to the documentation of TAPAS on the HuggingFace website. 
+
+
+## Training procedure
+
+### Preprocessing
+
+The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
+then of the form:
+
+```
+[CLS] Sentence [SEP] Flattened table [SEP]
+```
+
+### Fine-tuning
+
+The model was fine-tuned on 32 Cloud TPU v3 cores for 80,000 steps with maximum sequence length 512 and batch size of 512.
+In this setup, fine-tuning takes around 14 hours. The optimizer used is Adam with a learning rate of 2e-5, and a warmup 
+ratio of 0.05. See the [paper](https://arxiv.org/abs/2010.00571) for more details (appendix A2). 
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{herzig2020tapas,
+      title={TAPAS: Weakly Supervised Table Parsing via Pre-training}, 
+      author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos},
+      year={2020},
+      eprint={2004.02349},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR}
+}
+```
+
+```bibtex
+@misc{eisenschlos2020understanding,
+      title={Understanding tables with intermediate pre-training}, 
+      author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller},
+      year={2020},
+      eprint={2010.00571},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+```bibtex
+@inproceedings{2019TabFactA,
+  title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
+  author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
+  booktitle = {International Conference on Learning Representations (ICLR)},
+  address = {Addis Ababa, Ethiopia},
+  month = {April},
+  year = {2020}
+}
+```